A multi-strategy machine learning student modeling for intelligent tutoring systems: Based on blackboard approach

研究成果: Article

9 引文 斯高帕斯(Scopus)

摘要

Purpose: This study aims to propose a blackboard approach using multistrategy machine learning student modeling techniques to learn the properties of students' inconsistent behaviors during their learning process. Design/methodology/approach: These multistrategy machine learning student modeling techniques include inductive reasoning (similarity-based learning), deductive reasoning (explanation-based learning), and analogical reasoning (case-based reasoning). Findings: According to the properties of students' inconsistent behaviors, the ITS (intelligent tutoring system) may then adopt appropriate methods, such as intensifying teaching and practicing, to prevent their inconsistent behaviors from reoccurring. Originality/value: This research sets the learning object on a single student. After the inferences are accumulated from a group of students, what kinds of students tend to have inconsistent behaviors or under what conditions the behaviors happened for most students can be learned.

原文English
頁(從 - 到)274-293
頁數20
期刊Library Hi Tech
31
發行號2
DOIs
出版狀態Published - 2013 七月 15

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Library and Information Sciences

指紋 深入研究「A multi-strategy machine learning student modeling for intelligent tutoring systems: Based on blackboard approach」主題。共同形成了獨特的指紋。

  • 引用此